Data Sheet 1_Characterization of lactylation-related subtypes and diagnostic markers in my

Jie Bai's dataset contains gene expression data related to myocardial ischemia-reperfusion injury (MIRI), published on figshare in April 2026. It includes results from differential gene expression and coexpression network analyses identifying seven lactylation-related genes. The data underpins the development of diagnostic machine learning models and the classification of MIRI subtypes.

Use Cases

• Training diagnostic machine learning models based on identified lactylation-related gene expression patterns.
• Analyzing immune cell infiltration in myocardial injury based on CIBERSORT results mentioned in the description.
• Investigating molecular subtypes of myocardial ischemia-reperfusion injury based on consensus clustering results.
• Validating potential therapeutic targets related to lactylation modifications in cellular signaling pathways.

Strengths

• Identifies seven specific lactylation-related genes (G6pd, Tkt, Eif2s2, Pabpc1, Itgb2, Cenpf, Runx2) with expression validated in cell-based assays.
• Data is associated with a clear methodological pipeline including differential expression, coexpression network analysis, and machine learning.
• Published under a permissive CC-BY-4.0 license, allowing for reuse and redistribution.

Limitations

• Dataset is very small at 7.8 KB, indicating limited scope or a summary-level dataset rather than raw expression data.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for large-scale analysis.

Provenance

Source

Gene Expression Omnibus (GEO) database, as cited in the description.

Collection Method

Analyzed using differential gene expression, weighted gene coexpression network analysis (WGCNA), and machine learning models.

Freshness

Last updated 2026-04-21 05:34:15; freshness should be verified.